Active learning is a particular case of machine learning in which a learning algorithm can select when to query for a new label. By choosing the most useful samples for annotation, the number of samples for an effective machine learning model can be much smaller than with random sampling. The definition of useful depends on the implementation and is selected for a specific purpose. Typical goals are minimizing the generalization error or reducing the uncertainty of the model. [1]}, [2]}
